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Record W2170631771 · doi:10.1002/jbm.a.30561

Bioactive glass/polymer composite materials with mechanical properties matching those of cortical bone

2006· article· en· W2170631771 on OpenAlexaff
Veronika A. Koleganova, Suzanne M. Bernier, S. Jeffrey Dixon, Amin S. Rizkalla

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2006
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsWestern University
Fundersnot available
KeywordsMaterials scienceBioactive glassSilaneComposite materialBiocompatibilityPolymerComposite numberMethacrylateCortical boneCopolymer

Abstract

fetched live from OpenAlex

Stress shielding resulting from mismatch in dynamic mechanical properties contributes to the reduced stability of osseous implants. Our objective was to develop biocompatible composites having mechanical properties similar to those of cortical bone. Polymers of urethane dimethacrylate (UDMA) and 2-hydroxyethyl methacrylate (HEMA, 0-20%) and composites containing bioactive glass particles (70% SiO(2), 25% CaO, and 5% P(2)O(5)), with or without silane treatment were prepared. Young's moduli of composites containing silane-treated glass (16 GPa) were significantly greater than those of composites containing untreated glass (12-13 GPa) or of unfilled polymers (5-6 GPa). Bioactive glass reduced water sorption by the composites and incorporation of silane-treated glass prevented HEMA-induced increases in water sorption. Osteoblast-like cells attached equally well to UDMA polymer and composite containing silane-treated bioactive glass. Thus, silane treatment improved the mechanical properties of bioactive glass composites without compromising biocompatibility. This material has a Young's modulus comparable to that of cortical bone. Therefore, silane-treated bioactive glass composites, when used as implant or cement materials, would reduce stress shielding and improve implant stability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.288
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2006
Admission routes1
Has abstractyes

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